Aspect-level Sentiment Analysis Based On Bi-GRU Fusion Of Multiple Feature Information

Aspect-level sentiment analysis is a fine-grained sentiment analysis task whose goal is to determine the sentiment polarity of different aspects（aspects are also called attributes）contained in a text utterance.This kind of fine-grained sentiment analysis can more accurately tap into the textual information of consumers’ comments on products or services.For managers,they can use this textual sentiment information as a basis to more accurately improve the quality of products or service attitudes;and for other consumers,they can also use this textual sentiment information to understand the quality of the product,so as to achieve the purpose of purchasing the product.In aspect-level sentiment analysis tasks,most existing approaches use Long-Short Memory Neural Network（LSTM）combined with an attention mechanism model,which firstly,does not require artificial construction of text features to mine the required text information from text sequences,and secondly,combines This model firstly,does not need to construct text features artificially to mine the required text information from text sequences,and secondly,combines the attention mechanism to assign different weights to text features.However,the LSTM neural network model is prone to problems such as long training time and overfitting when training longer text sequences,and for the model that introduces the attention mechanism,it implicitly models the relationship between attributes and emotional expressions in the context,and can only obtain textual emotional information while ignoring the use of grammatical information,which leads to a less comprehensive representation of text features and makes it difficult for the model to correctly match aspects attributes and sentiment attributes.Therefore,to address the above problems,this paper proposes a neural network model based on Bi-GRU fusing multiple types of information（BRGN Model）.The word vectors obtained from four pre-training models,namely Word2 vec,Glove,ELMo and BERT,are used for sentiment classification comparison experiments to verify the rationality of the BERT pre-training model to construct suitable word vectors as the word embedding layer of the BRGN model.In order to solve the drawbacks of the LSTM neural network model,while taking into account the connection between text contexts,the BRGN model builds bidirectional GRU neural network layers.The word vector obtains the semantic information of words through the bidirectional GRU neural network layer;meanwhile,the position information of words in the text sequence is used to focus on the influence of words closer to the position of aspectual words on the emotional polarity of aspectual words.The word vector obtains the position characteristics of word context through the bidirectional GRU neural network layer and stores the position information of words in the position memory layer.The incorporation of external knowledge obtained through syntactic analysis using the graph convolutional neural network layer enables the model to perceive the syntactic structural information of text sequences.The syntactic dependency tree of the text in the form of an adjacency matrix is used as the input to the graph convolutional neural network,and then the graph convolutional neural network processes the graph structure information data represented by the adjacency matrix,extracts the features of the adjacent contacts,and establishes relationships between aspect words and sentiment words,thus assisting the model in aspect-level sentiment analysis.In order to make full use of the features such as semantic information of text stored in the memory network,location information and syntactic information obtained from the graph convolutional neural network model,this paper proposes a sentiment attention layer that takes into account auxiliary information such as syntax.By increasing the weight of the lexical vector representation of the sentiment word corresponding to the current aspect word,the sentiment words related to the aspect word in the text sequence are filtered,and the sentiment classification layer is assisted to determine the sentiment classification layer is used to determine the sentiment tendency of the text.Finally,in order to verify the prediction performance of the proposed BRGN model,the experimental results on Restaurant,Laptop and Twitter datasets show that the proposed model achieves better accuracy and F1 values on the above three datasets,thus confirming the effectiveness of the model.The effects of the number of layers of the graph convolutional neural network on the performance of the model are also verified,and the reliability of the model is confirmed by the example analysis that the BRGN model proposed in this paper can efficiently predict the sentiment polarity belonging to different aspects in text comments.